Functional assessment or monitoring of a person's health status, physical abilities, mental abilities, or recuperation after injury, hospitalization and treatment is of primary concern in most branches of medicine, including geriatrics, rehabilitation and physical therapy, neurology and orthopaedics, nursing and elder care.
Investigations have found that an individual's functional ability is actually environment-specific, since function increases when subjects are in familiar surroundings due to reduced confusion. Also, one-time assessment of function does not allow for assessment of variability of functional performance over the course of a day or several days, nor does it allow for assessment of change which is important in determining the adequacy of certain clinical services and treatments (such as rehabilitation) following functional loss.
A consensus therefore exists that it is preferable to assess or monitor independent functioning of a person at their home or within familiar surroundings.
A level of independent function is commonly indicated by the quality in which Activities of Daily Living (ADLs) are performed. ADLs refer to the most common activities that people perform during a day. Therefore, a reduced quality in the ADLs can be an indicator for care needed. For example, an anomaly in the regular performance of one or more ADLs can serve as warning for special attention.
Devices and systems have been developed to monitor the ADLs of individuals as they live independently in their own home or within familiar surroundings. For example, one such known system for detecting activities of daily living of a person system comprises three main components: (i) a sensor system that collects information about the person's activities and behaviours; (ii) an intelligence (or information processing) system that interprets the sensor signals for determination of ADL behaviour; and (iii) a user interface system that enables care givers to inspect the interpreted (processed) information. The intelligence system typically makes use of computational techniques known in the art as artificial intelligence. The system may be supported by conventional technologies for data collection, transmission, and storage.
In practice, however, a major difficulty is encountered by the wide range of variations that can happen in actual care cases. Since there are so many possible circumstances, situations and contexts that can occur in daily life, it is common to employ numerous sensors in an attempt to capture enough information about a person's activities to enable identification of specific activities.
The ever-increasing complexity in striving to cover all possible contexts and situations requires more elaborate and detailed information to be collected, processed, interpreted and/or communicated.
Furthermore, scenarios can result in sensor signals that are indicative of an activity, such as eating/drinking for example, even though the activity has not been undertaken. Thus, despite being complex and costly, conventional ADL monitoring concepts can exhibit low accuracy. This is particularly the case for the ADL of eating/drinking because there are lots of scenarios that may be indicative of a monitored person eating/drinking whilst not actually guaranteeing that the person has indeed consumed liquid and/or food.